Do Competitively
This is a heavyweight quality-over-speed approach that spins up four parallel agents: three to generate competing solutions and a meta-judge to create custom evaluation rubrics. Each generator does self-critique loops before submitting. Then three judges evaluate all solutions using the rubrics, with verification steps to catch their own errors. The system adaptively decides whether to polish the winner, synthesize the best parts from multiple solutions, or start over if everything scored poorly. It claims 15-20% cost savings by skipping full synthesis when there's a clear winner. Use this when correctness matters more than time, like critical infrastructure code or high-stakes architecture decisions. The strictness of the orchestration is unusual: it literally threatens termination if you deviate from the process.
- skills.sh installs
- 1.2K
- GitHub stars
- 1.8K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add neolabhq/context-engineering-kit --skill do-competitively --agent claude-codeInstalls into .claude/skills of the current project.
Files
do-competitively
Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis to produce superior results by combining the best elements from parallel implementations. This command implements the Generate-Critique-Synthesize (GCS) pattern with adaptive strategy selection for high-stakes tasks where quality matters more than speed. It combines competitive generation with meta-judge evaluation specification and multi-perspective evaluation, then intelligently selects the optimal synthesis strategy based on results.Key features:
- Self-critique loops in generation (Constitutional AI)
- Structured evaluation - Meta-judge produces tailored rubrics before judging
- Verification loops in evaluation (Chain-of-Verification)
- Adaptive strategy: polish clear winners, synthesize split decisions, redesign failures
- Average 15-20% cost savings through intelligent strategy selection
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- Repository
- github.com/neolabhq/context-engineering-kit
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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